Configure a Keras model for training

Arguments

object

Model object to compile.

optimizer

Name of optimizer or optimizer instance.

loss

Name of objective function or objective function. If the model
has multiple outputs, you can use a different loss on each output by
passing a dictionary or a list of objectives. The loss value that will be
minimized by the model will then be the sum of all individual losses.

metrics

List of metrics to be evaluated by the model during training
and testing. Typically you will use metrics='accuracy'. To specify
different metrics for different outputs of a multi-output model, you could
also pass a named list such as metrics=list(output_a = 'accuracy').

loss_weights

Optional list specifying scalar coefficients to weight
the loss contributions of different model outputs. The loss value that will
be minimized by the model will then be the weighted sum of all indvidual
losses, weighted by the loss_weights coefficients.

sample_weight_mode

If you need to do timestep-wise sample weighting
(2D weights), set this to "temporal". NULL defaults to sample-wise
weights (1D). If the model has multiple outputs, you can use a different
sample_weight_mode on each output by passing a list of modes.

weighted_metrics

List of metrics to be evaluated and weighted
by sample_weight or class_weight during training and testing

target_tensors

By default, Keras will create a placeholder for the
model's target, which will be fed with the target data during
training. If instead you would like to use your own
target tensor (in turn, Keras will not expect external
data for these targets at training time), you
can specify them via the target_tensors argument. It should be
a single tensor (for a single-output sequential model),

...

When using the Theano/CNTK backends, these arguments
are passed into K.function. When using the TensorFlow backend,
these arguments are passed into tf$Session()$run.